Following the design pipeline of [1]}, we define the task of future influencer detection in this paper as a supervised node-level learning problem on a dynamic heterogeneous undirected network. A typical network learning process consists of an encoder and decoder [2]}. The encoder part of the model is aimed at learning node embeddings while the decoder part is used to solve a prediction task, e.g., node classification. [3]} survey different encoder-decoder architectures that exist for supervised dynamic graph learning and classify them into Discrete Time Dynamic Graph (DTDG) learning that uses network snapshots and Continuous Time Dynamic Graph (CTDG) learning that deals with an updating event stream in a network, e.g., the Temporal Graph Networks framework for deep learning on dynamic network represented as sequences of timed events [4]}. Following their taxonomy, we capture the network topology at each timestamp by applying the DTDG encoder for attributed static networks, namely, Graph Convolutional Networks (GCNs) [5]} and Graph Attention Networks (GATs) [6]}. We capture the dynamic nature of the networks by employing the models from the RNN family as a DTDG decoder. We also compare these models with baseline models, namely, the PageRank+RNN, static GNNs and dynamic non-GNN models.
